In recent years and continuing, techniques for realizing high-rate and high-quality wireless transmission have been discussed. Such techniques include error correction coding, multi-level modulation (such as 16 QAM or 64 QAM), and space division multiplex transmission using multiple transmission antennas.
FIG. 1A and FIG. 1B are schematic block diagrams of a transmitter with multiple transmission antennas and functions of error correction and multi-level modulation. In the example shown in FIG. 1A, the transmission bitstream is encoded by the coder 110, and the code stream is divided into multiple pieces by the serial-to-parallel (S/P) converter 111 according to the number of transmission antennas. Each of the divided pieces is supplied to one of the modulators 112-1 through 112-N and is subjected to QAM multi-level modulation. Then the modulated pieces are space-division-multiplexed and transmitted from transmission antennas 101-1 through 101-N. The multiple signals transmitted from N antennas are received by M antennas (201-1 through 201-M) at the receiving end, and received signals r1-rM are extracted. The S/P converted bit “cn,k” at the transmission end denotes the k-th code bit for the n-th antenna.
FIG. 1B is a more specific example of the structure shown in FIG. 1A, and illustrates a transmitter using four transmission antennas, convolutional coding rate of ½, and QAM modulation. The 4-bit information is input to the half-rate coder 111, and converted into an 8-bit stream, which stream is then divided into four 2-bit data items. The 2-bit data items are subjected to multi-level modulation in the associated 4QAM modulators 112-1 through 112-4, and transmitted from the transmission antennas 101-1 through 101-4 in a space division multiplexing manner. It should be noted that only one symbol-duration data flow is depicted in the examples of FIG. 1A and FIG. 1B.
In data transmission using multiple antennas illustrated in FIG. 1A and FIG. 1B, the vector “r” of the received signal is expressed by Equation (1), using channel matrix H, transmission symbol vector “s”, and noise vector “n”,
                                                        r              =                            ⁢                              [                                                                                                    r                        1                                                                                                                        ⋮                                                                                                                          r                        M                                                                                            ]                                                                                        =                            ⁢                                                                    [                                                                                                                        h                            11                                                                                                    …                                                                                                      h                                                          1                              ⁢                              N                                                                                                                                                                            ⋮                                                                          ⋰                                                                          ⋮                                                                                                                                                  h                                                          M                              ⁢                                                                                                                          ⁢                              1                                                                                                                                …                                                                                                      h                                                          M                              ⁢                                                                                                                          ⁢                              N                                                                                                                                            ]                                    ·                                      [                                                                                                                        s                            1                                                                                                                                                ⋮                                                                                                                                                  s                            N                                                                                                                ]                                                  +                                  [                                                                                                              n                          1                                                                                                                                    ⋮                                                                                                                                      n                          M                                                                                                      ]                                                                                                        ≡                            ⁢                                                H                  ·                  s                                +                n                                                                        (        1        )            where index M denotes the number of receiving antennas, and N denotes the number of transmission antennas. The variance of noise nm is represented by σ2. Entry hmn of the matrix indicates the channel between the n-th transmission antenna and the m-th receiving antenna.
A known receiver structure for processing the received signal “r” to decode the transmitted bitstream is a Max-LOG-MAP (Maximum A posteriori Probability) receiver. With this structure, metrics (−∥r−H·s∥2) of all the transmitted bit patterns are calculated, and logarithmic likelihood (hereinafter, referred to simply as “likelihood”) Ln,k of transmitted code bit cn,k is determined from Equation (2), using the calculated metric values.
                              L                      n            ,            k                          =                              max                                          S                ⁢                                                                  ⁢                                  s                  .                  t                  .                                      c                                          n                      ,                      k                                                                                  =              1                                ⁢                                    {                              -                                                                                                r                      -                                              H                        ·                        s                                                                                                  2                                            }                        ⁢                                          -                                                     max                                                                              S                  ⁢                                                                          ⁢                                      s                    .                    t                    .                                          c                                              n                        ,                        k                                                                                            =                0                                      ⁢                          {                              -                                                                                                r                      -                                              H                        ·                        s                                                                                                  2                                            }                                                          (        2        )            The subscript “S s.t.cn,k=1” shown in the first term of the right-hand-side of Equation (2) represents a set of symbol sequence candidates having a bit cn,k=1 among the whole set S of all possible symbol sequence candidates. The first term of the right-hand-side of Equation (2) represents the maximum metric in the symbol sequence candidates having a bit cn,k=1. The subscript “S s.t.cn,k=0” shown in the second term of the right-hand-side of Equation (2) represents a set of symbol sequence candidates having a bit cn,k=0 among the whole set S of all possible symbol sequence candidates. The second term of the right-hand-side of Equation (2) represents the maximum metric in the symbol sequence candidates having a bit cn,k=0.
The determined likelihood of the code bitstream is input to a decoder and decoded. For the likelihood estimation, all the bitstream candidates have to be calculated. As the number of levels of multi-level modulation or the number of transmission antennas increases, its computational amount increases exponentially. To avoid this problem, it is proposed to perform pre-processing prior to likelihood estimation. For example, a receiver using a reduced number of bitstream candidates is proposed, which receiver is designed so as to reduce the processing workload by narrowing the bitstream candidates to a certain extent in advance and estimating likelihood for the metrics of the narrowed candidates. See, for example,    (1) Bertrand M. Hochwald, et. al., “Achieving Near-Capacity on a Multiple-Antenna Channel”, IEEE Transactions on Communication, Vol. 51, No. 3 March 2003; and    (2) H. Kawai, et al., “Complexity-reduced Maximum Likelihood Detection Based on Replica Candidate Selection with QR decomposition Using Pilot-Assisted Channel Estimation and Ranking for MIMO Multiplexing Using OFCDM”, IEICE Technical Report. RCS, March 2004, pp. 55-60.
FIG. 2 is a schematic diagram illustrating the basic structure of a conventional receiver based on a reduced number of bitstream candidates. At the transmission end, an input bit stream is encoded by coder 110, and divided pieces of the coded bitstream are modulated by modulator 112 and transmitted from transmission antennas 101-1 through 101-N. Signals received at multiple receiving antennas 201-1 through 201-M are supplied to a reduced bitstream candidate estimator 202. If n-bit information is generated, prior to division of the bitstream (e.g., n/M bits per transmission antenna), at the transmission end, then the reduced bitstream candidate estimator 202 selects only Z bitstream candidates, which are likely to have high reliability, among 2n bit patterns and calculates metrics for the selected candidates. The selected bitstream candidates and the associated metrics are supplied to a bit-based likelihood estimator 203, which estimates likelihood for each bit based on Equation (2). The likelihood of each bit is supplied to the soft-input decoder 210 and decoding is performed using the likelihood.
FIG. 3 illustrates the likelihood estimation technique disclosed in the above-described publication (1). In this example, an 8-bit code sequence is to be decoded. The reduced bitstream candidate estimator 202 estimates four patterns as bitstream candidates among 256 (which equals 28) bit patterns, and calculates the metric (which equals (−∥r−H·s∥2) for each of the selected candidates. Metrics of candidate 1, 2, 3, and 4 are −5, −6, −30, and −100, respectively. At this point of time, the bitstream candidates are reduced down to 1/64 of the possible bitpatterns. As the bitstream candidate reducing technique, publication (1) uses a sphere decoding technique, and publication (2) uses M algorithm (deterministic algorithm).
The bit-based likelihood estimator 205 includes a likelihood calculation unit 206 and a likelihood clipping unit 207. The likelihood calculation unit 206 calculates a log likelihood ratio (LLR, which is also referred to simply as “likelihood”) of each bit of the bitstream from Equation (2), based on the four bitstream candidates 1-4 and the associated metrics. For the first bit, all the four bitstream candidates take “1”, and accordingly, the first term of the right-hand-side of Equation (2), which represents the maximum metric corresponding to “1”, becomes −5. The second term of the right-hand-side of Equation (2) represents the maximum metric for “0”; however, there is no candidate in the selected ones that has “0” at the first bit and no metric is found. Therefore, a predetermined fixed value is used as a substitute for the second term metric. In this example, fixed value X is set to −1000 (X=−1000). The reason for choosing a very small value (such as −1000) as the fixed value X is based on inference that there is little possibility that “0” has been transmitted because there is no metric corresponding to “0”. If the probability of “0” transmission is high, the metric will take a greater value, but is not beyond zero. By using the fixed value X, the likelihood LLR of the first bit becomes −5−(−1000)=995. Similarly, likelihood is calculated for each of the second through the eighth bits.
The likelihood clipping unit 207 clips a likelihood whose absolute value exceeds a prescribed value down to a prescribed clipping value. For example, the clipping value is set to 30 in advance (C=30), and if the absolute value of the calculated likelihood exceeds the clipping value, that likelihood is clipped to 30. The likelihood values having been subjected to the clipping operation are input to the soft-input decoder 210, and a transmitted bitstream is decoded. The significance of the clipping operation for restricting the absolute value of likelihood is as follows. If the calculated likelihood takes a large value, such as 995, as compared with other likelihood values, this strongly suggests that +1 has been transmitted. However, if −1 has actually been transmitted, 995 becomes a huge error which will adversely affect the subsequent decoding characteristic. This problem does not exist in likelihood estimation using all bitstream candidates, such as a MAX-LOG-MAP method; however, it may happen when a reduced bitstream candidate based receiver is used.
In this manner, with the conventional bitstream-candidate reducing scheme, the bit-based likelihood estimator 205 produces likelihood of each bit, using the reduced number of bit patterns and the associated metrics estimated by the reduced bitstream candidate estimator 202. Among the selected bit patterns, those with relatively large metrics (such as candidates 1 and 2 in FIG. 3) are reliable, but those with small metrics (such as candidate 4) are deemed to be less reliable because reduced bitstream candidate estimator 20 may operate erroneously and output less reliable candidates.
If likelihood is calculated using metrics of less reliable bit patterns, accuracy of likelihood estimation is degraded, and advantages of the subsequent soft-input decoder can be not sufficiently pulled out.